Announced in 2016, Gym is an open-source Python library created to facilitate the development of support learning algorithms. It aimed to standardize how environments are specified in AI research study, making published research study more quickly reproducible [24] [144] while offering users with a simple user interface for engaging with these environments. In 2022, new advancements of Gym have been transferred to the library Gymnasium. [145] [146]
Gym Retro

Released in 2018, Gym Retro is a platform for reinforcement knowing (RL) research study on video games [147] using RL algorithms and study generalization. Prior RL research focused mainly on optimizing agents to resolve single jobs. Gym Retro provides the ability to generalize in between games with comparable ideas but various appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic representatives initially lack knowledge of how to even walk, but are provided the goals of finding out to move and to press the opposing agent out of the ring. [148] Through this adversarial learning process, the agents learn how to adjust to altering conditions. When an agent is then removed from this virtual environment and positioned in a brand-new virtual environment with high winds, the representative braces to remain upright, suggesting it had actually discovered how to balance in a generalized way. [148] [149] OpenAI’s Igor Mordatch argued that competitors in between representatives could create an intelligence “arms race” that might increase a representative’s capability to operate even outside the context of the competition. [148]
OpenAI 5

OpenAI Five is a group of five OpenAI-curated bots utilized in the competitive five-on-five video game Dota 2, that learn to play against human players at a high ability level totally through experimental algorithms. Before becoming a team of 5, the very first public presentation happened at The International 2017, the annual best champion competition for the video game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live one-on-one match. [150] [151] After the match, CTO Greg Brockman explained that the bot had discovered by playing against itself for 2 weeks of actual time, and that the knowing software was a step in the instructions of producing software that can deal with complicated jobs like a surgeon. [152] [153] The system utilizes a type of reinforcement knowing, as the bots discover with time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as eliminating an enemy and taking map goals. [154] [155] [156]
By June 2018, the ability of the bots expanded to play together as a full group of 5, and they had the ability to beat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition matches against expert gamers, but ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world champs of the game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots’ last public look came later on that month, where they played in 42,729 total video games in a four-day open online competition, winning 99.4% of those video games. [165]
OpenAI 5’s systems in Dota 2’s bot gamer shows the challenges of AI systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has actually demonstrated making use of deep support knowing (DRL) agents to attain superhuman proficiency in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes maker finding out to train a Shadow Hand, a human-like robot hand, to manipulate physical things. [167] It learns completely in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI dealt with the things orientation issue by utilizing domain randomization, a simulation technique which exposes the learner to a variety of experiences rather than attempting to fit to truth. The set-up for Dactyl, aside from having movement tracking cams, also has RGB cameras to permit the robotic to control an arbitrary item by seeing it. In 2018, OpenAI revealed that the system had the ability to manipulate a cube and an octagonal prism. [168]
In 2019, OpenAI demonstrated that Dactyl could resolve a Rubik’s Cube. The robot had the ability to fix the puzzle 60% of the time. Objects like the Rubik’s Cube introduce complicated physics that is harder to model. OpenAI did this by improving the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of creating gradually more challenging environments. ADR varies from manual domain randomization by not needing a human to specify randomization varieties. [169]
API

In June 2020, OpenAI revealed a multi-purpose API which it said was “for accessing new AI designs developed by OpenAI” to let designers call on it for “any English language AI task”. [170] [171]
Text generation

The business has promoted generative pretrained transformers (GPT). [172]
OpenAI’s initial GPT design (“GPT-1”)

The original paper on generative pre-training of a transformer-based language model was composed by Alec Radford and his colleagues, and published in preprint on OpenAI’s website on June 11, 2018. [173] It showed how a generative model of language could obtain world understanding and process long-range dependences by pre-training on a diverse corpus with long stretches of contiguous text.

GPT-2

Generative Pre-trained Transformer 2 (“GPT-2”) is a not being watched transformer language model and the follower to OpenAI’s initial GPT model (“GPT-1”). GPT-2 was revealed in February 2019, with only limited demonstrative versions initially launched to the public. The full variation of GPT-2 was not instantly launched due to concern about prospective misuse, consisting of applications for writing fake news. [174] Some specialists expressed uncertainty that GPT-2 presented a significant risk.

In reaction to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to find “neural fake news”. [175] Other researchers, such as Jeremy Howard, alerted of “the innovation to completely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would hush all other speech and be difficult to filter”. [176] In November 2019, OpenAI released the complete variation of the GPT-2 language design. [177] Several websites host interactive presentations of various instances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2’s authors argue not being watched language models to be general-purpose learners, highlighted by GPT-2 attaining cutting edge precision and perplexity on 7 of 8 zero-shot tasks (i.e. the model was not additional trained on any task-specific input-output examples).

The corpus it was trained on, called WebText, contains somewhat 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It prevents certain problems encoding vocabulary with word tokens by utilizing byte pair encoding. This allows representing any string of characters by encoding both private characters and multiple-character tokens. [181]
GPT-3

First explained in May 2020, Generative Pre-trained [a] Transformer 3 (GPT-3) is a not being watched transformer language model and the successor to GPT-2. [182] [183] [184] OpenAI stated that the full version of GPT-3 contained 175 billion specifications, [184] 2 orders of magnitude bigger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 designs with as couple of as 125 million criteria were also trained). [186]
OpenAI stated that GPT-3 was successful at certain “meta-learning” jobs and could generalize the function of a single input-output pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer learning between English and Romanian, and in between English and German. [184]
GPT-3 drastically improved benchmark results over GPT-2. OpenAI warned that such scaling-up of language models could be approaching or encountering the fundamental capability constraints of predictive language models. [187] Pre-training GPT-3 needed several thousand petaflop/s-days [b] of calculate, compared to 10s of petaflop/s-days for the full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not instantly released to the public for concerns of possible abuse, although OpenAI planned to permit gain access to through a paid cloud API after a two-month free private beta that started in June 2020. [170] [189]
On September 23, 2020, GPT-3 was licensed solely to Microsoft. [190] [191]
Codex

Announced in mid-2021, Codex is a descendant of GPT-3 that has actually furthermore been trained on code from 54 million GitHub repositories, [192] [193] and is the AI powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was released in personal beta. [194] According to OpenAI, the model can develop working code in over a lots programs languages, most effectively in Python. [192]
Several problems with glitches, style defects and security vulnerabilities were mentioned. [195] [196]
GitHub Copilot has been implicated of producing copyrighted code, with no author attribution or license. [197]
OpenAI announced that they would discontinue assistance for Codex API on March 23, 2023. [198]
GPT-4

On March 14, 2023, OpenAI announced the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They announced that the updated innovation passed a simulated law school bar test with a rating around the leading 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 might also check out, analyze or produce up to 25,000 words of text, and write code in all major shows languages. [200]
Observers reported that the model of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based version, with the caution that GPT-4 retained some of the problems with earlier modifications. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has declined to expose different technical details and statistics about GPT-4, such as the precise size of the model. [203]
GPT-4o

On May 13, 2024, wiki.snooze-hotelsoftware.de OpenAI announced and launched GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained advanced lead to voice, multilingual, and vision criteria, setting new records in audio speech recognition and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) benchmark compared to 86.5% by GPT-4. [207]
On July 18, 2024, OpenAI released GPT-4o mini, a smaller version of GPT-4o replacing GPT-3.5 Turbo on the ChatGPT interface. Its API costs $0.15 per million input tokens and $0.60 per million output tokens, compared to $5 and $15 respectively for GPT-4o. OpenAI anticipates it to be particularly beneficial for enterprises, start-ups and designers seeking to automate services with AI representatives. [208]
o1

On September 12, 2024, wavedream.wiki OpenAI released the o1-preview and o1-mini models, forum.pinoo.com.tr which have actually been designed to take more time to believe about their actions, resulting in greater precision. These designs are especially reliable in science, coding, and reasoning jobs, and were made available to ChatGPT Plus and Employee. [209] [210] In December 2024, o1-preview was replaced by o1. [211]
o3

On December 20, 2024, OpenAI revealed o3, the follower of the o1 reasoning design. OpenAI also unveiled o3-mini, a lighter and faster variation of OpenAI o3. As of December 21, 2024, this model is not available for public usage. According to OpenAI, they are evaluating o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security scientists had the chance to obtain early access to these designs. [214] The design is called o3 rather than o2 to prevent confusion with telecommunications services company O2. [215]
Deep research

Deep research is a representative established by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI’s o3 design to carry out extensive web browsing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With searching and Python tools allowed, it reached an accuracy of 26.6 percent on HLE (Humanity’s Last Exam) criteria. [120]
Image classification

CLIP

Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a model that is trained to evaluate the semantic similarity between text and images. It can notably be utilized for image classification. [217]
Text-to-image

DALL-E

Revealed in 2021, DALL-E is a Transformer model that produces images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter version of GPT-3 to interpret natural language inputs (such as “a green leather handbag shaped like a pentagon” or “an isometric view of a sad capybara”) and create matching images. It can develop images of realistic objects (“a stained-glass window with an image of a blue strawberry”) along with items that do not exist in truth (“a cube with the texture of a porcupine”). Since March 2021, no API or code is available.

DALL-E 2

In April 2022, OpenAI announced DALL-E 2, an updated version of the design with more sensible results. [219] In December 2022, OpenAI published on GitHub software for Point-E, a new basic system for converting a text description into a 3-dimensional model. [220]
DALL-E 3

In September 2023, OpenAI announced DALL-E 3, a more effective model much better able to produce images from intricate descriptions without manual timely engineering and render complex details like hands and text. [221] It was launched to the public as a ChatGPT Plus function in October. [222]
Text-to-video

Sora

Sora is a text-to-video design that can generate videos based upon brief detailed triggers [223] in addition to extend existing videos forwards or in reverse in time. [224] It can create videos with resolution up to 1920x1080 or 1080x1920. The optimum length of created videos is unknown.

Sora’s development team named it after the Japanese word for “sky”, to signify its “endless innovative capacity”. [223] Sora’s technology is an adjustment of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos in addition to copyrighted videos accredited for that function, however did not reveal the number or the precise sources of the videos. [223]
OpenAI showed some Sora-created high-definition videos to the general public on February 15, engel-und-waisen.de 2024, stating that it might as much as one minute long. It also shared a technical report highlighting the approaches used to train the model, and the design’s abilities. [225] It acknowledged a few of its shortcomings, including battles simulating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos “excellent”, wavedream.wiki but kept in mind that they should have been cherry-picked and may not represent Sora’s common output. [225]
Despite uncertainty from some academic leaders following Sora’s public demonstration, noteworthy entertainment-industry figures have revealed significant interest in the technology’s capacity. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the technology’s capability to generate sensible video from text descriptions, mentioning its possible to revolutionize storytelling and content development. He said that his enjoyment about Sora’s possibilities was so strong that he had actually chosen to stop briefly plans for broadening his Atlanta-based motion picture studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech acknowledgment design. [228] It is trained on a large dataset of diverse audio and surgiteams.com is also a multi-task design that can carry out multilingual speech acknowledgment along with speech translation and language recognition. [229]
Music generation

MuseNet

Released in 2019, MuseNet is a deep neural net trained to forecast subsequent musical notes in MIDI music files. It can create tunes with 10 instruments in 15 styles. According to The Verge, a tune generated by MuseNet tends to start fairly however then fall into mayhem the longer it plays. [230] [231] In pop culture, initial applications of this tool were used as early as 2020 for the internet psychological thriller Ben Drowned to create music for the titular character. [232] [233]
Jukebox

Released in 2020, Jukebox is an open-sourced algorithm to generate music with vocals. After training on 1.2 million samples, the system accepts a genre, artist, and a snippet of lyrics and outputs song samples. OpenAI specified the songs “reveal regional musical coherence [and] follow standard chord patterns” but acknowledged that the songs do not have “familiar larger musical structures such as choruses that repeat” which “there is a significant gap” in between Jukebox and human-generated music. The Verge specified “It’s technically impressive, even if the outcomes sound like mushy variations of tunes that might feel familiar”, while Business Insider stated “remarkably, some of the resulting songs are catchy and sound legitimate”. [234] [235] [236]
Interface

Debate Game

In 2018, OpenAI launched the Debate Game, which teaches devices to debate toy issues in front of a human judge. The function is to research whether such an approach might help in auditing AI decisions and in establishing explainable AI. [237] [238]
Microscope

Released in 2020, Microscope [239] is a collection of visualizations of every considerable layer and nerve cell of 8 neural network designs which are typically studied in interpretability. [240] Microscope was developed to examine the features that form inside these neural networks quickly. The designs consisted of are AlexNet, VGG-19, different variations of Inception, and different versions of CLIP Resnet. [241]
ChatGPT

Launched in November 2022, ChatGPT is an expert system tool developed on top of GPT-3 that offers a conversational interface that allows users to ask concerns in natural language. The system then responds with a response within seconds.